BatchToSpaceND

tensorflow C++ API

tensorflow::ops::BatchToSpaceND

BatchToSpace for N-D tensors of type T.

Summary

This operation reshapes the "batch" dimension 0 into M + 1 dimensions of shape block_shape + [batch] , interleaves these blocks back into the grid defined by the spatial dimensions [1, ..., M] , to obtain a result with the same rank as the input. The spatial dimensions of this intermediate result are then optionally cropped according to crops to produce the output. This is the reverse of SpaceToBatch. See below for a precise description.

Arguments:

• scope: A Scope object
• input: N-D with shape input_shape = [batch] + spatial_shape + remaining_shape , where spatial_shape has M dimensions.
• block_shape: 1-D with shape [M] , all values must be >= 1.
• crops: 2-D with shape [M, 2] , all values must be >= 0. crops[i] = [crop_start, crop_end] specifies the amount to crop from input dimension i + 1 , which corresponds to spatial dimension i . It is required that crop_start[i] + crop_end[i] <= block_shape[i] * input_shape[i + 1].

This operation is equivalent to the following steps:

1. Reshape input to reshaped of shape: [block_shape[0], ..., block_shape[M-1], batch / prod(block_shape), input_shape[1], ..., input_shape[N-1]]
2. Permute dimensions of reshaped to produce permuted of shape [batch / prod(block_shape),input_shape[1], block_shape[0], ..., input_shape[M], block_shape[M-1],input_shape[M+1], ..., input_shape[N-1]]
3. Reshape permuted to produce reshaped_permuted of shape [batch / prod(block_shape),input_shape[1] * block_shape[0], ..., input_shape[M] * block_shape[M-1],input_shape[M+1], ..., input_shape[N-1]]
4. Crop the start and end of dimensions [1, ..., M] of reshaped_permuted according to crops to produce the output of shape: [batch / prod(block_shape),input_shape[1] * block_shape[0] - crops[0,0] - crops[0,1], ..., input_shape[M] * block_shape[M-1] - crops[M-1,0] - crops[M-1,1],input_shape[M+1], ..., input_shape[N-1]]

Some examples:

(1) For the following input of shape[4, 1, 1, 1],block_shape = [2, 2], andcrops = [[0, 0], [0, 0]]:

``` [[[[1]]], [[[2]]], [[[3]]], [[[4]]]] ```

The output tensor has shape[1, 2, 2, 1]and value:

``` x = [[[[1], [2]], [[3], [4]]]] ```

(2) For the following input of shape[4, 1, 1, 3],block_shape = [2, 2], andcrops = [[0, 0], [0, 0]]:

``` [[[1, 2, 3]], [[4, 5, 6]], [[7, 8, 9]], [[10, 11, 12]]] ```

The output tensor has shape[1, 2, 2, 3]and value:

``` x = [[[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]] ```

(3) For the following input of shape[4, 2, 2, 1],block_shape = [2, 2], andcrops = [[0, 0], [0, 0]]:

``` x = [[[[1], [3]], [[9], [11]]], [[[2], [4]], [[10], [12]]], [[[5], [7]], [[13], [15]]], [[[6], [8]], [[14], [16]]]] ```

The output tensor has shape[1, 4, 4, 1]and value:

``` x = [[[1], [2], [3], [4]], [[5], [6], [7], [8]], [[9], [10], [11], [12]], [[13], [14], [15], [16]]] ```

(4) For the following input of shape[8, 1, 3, 1],block_shape = [2, 2], andcrops = [[0, 0], [2, 0]]:

``` x = [[[[0], [1], [3]]], [[[0], [9], [11]]], [[[0], [2], [4]]], [[[0], [10], [12]]], [[[0], [5], [7]]], [[[0], [13], [15]]], [[[0], [6], [8]]], [[[0], [14], [16]]]] ```

The output tensor has shape[2, 2, 4, 1]and value:

``` x = [[[[1], [2], [3], [4]], [[5], [6], [7], [8]]], [[[9], [10], [11], [12]], [[13], [14], [15], [16]]]] ```

Returns:

• Output: The output tensor.

BatchToSpaceND block

Source link :https://github.com/EXPNUNI/enuSpaceTensorflow/blob/master/enuSpaceTensorflow/tf_array_ops.cpp

Argument:

• Scope scope : A Scope object (A scope is generated automatically each page. A scope is not connected.)
• Input input : connect Input or const shape node. (N-D tensor with shape)
• Input block_shape: connect Input or const shape node. (1-D tensor with shape)
• Input crops : connect Input or const shape node. (2-D tensor with shape)

Return:

• Output output : Output object of BatchToSpaceND class object.

Result:

• std::vector(Tensor) result_output : Returned object of executed result by calling session. (N-D tensor with shape)

Using Method

※ N차원의 input 을 재배치하는 기능을 한다. BatchToSpace와 다른점은 crops로 input을 잘라낼 수 있다.